Finding Scarcity In An Abundance of Analytics

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The law of scarcity is a building block of commerce. It suggests that a desirable item in short supply is more valuable than one that is more readily available.

How does this apply to our digital economy? Indeed, how should we think about scarcity when software-as-a-service companies are obsessing over scale and growth?

The dichotomy I want to illustrate here is that of scale vs value. There is an idea with technology companies that we should create something once and then sell it many times. That is true with platform software (e.g., iTunes), but it is not valid with providers of data. With data or the insight derived from it, the value of that insight diminishes with the number of people who have access to it. This is the law of scarcity as it pertains to information.

The maturity of cloud technology has boosted the opportunity that geospatial technology affords the business community. We can build pipelines of data in the sky. We can control the flows of that data with manipulation engines, transformations and fusion techniques. We can identify features from imagery, then use those features to feed machine learning algorithms. We can use those algorithms to filter and interpret torrents of data flowing at a rate orders-of-magnitude higher than a team of humans could consume.

Geospatial can now deliver what we have always promised: the near real-time, remote monitoring and analysis of assets. But how should we be providing this capability? An obvious place for us to look has been the financial markets. Providing traders and analysts with new insight with which to develop “signal.” The commercial geospatial and remote sensing industry, which has been chasing access to the elusive “Bloomberg Terminal,” should, however, consider the pricing effect of abundance.

Of course, there are base data acquisition companies which must exist to support the creation of geospatial analytics. Those are the companies that are creating streams of base data from which the analytics companies are drawing their insight. Products of this nature will always be necessary, indeed foundational for discerning any geospatial insight. Including the satellite, LiDAR, RADAR, metrological and mapping companies, these are the companies that know how to create building blocks from which to derive vertically-focused insight. These companies will remain mostly unchallenged by scarcity, but they will be subject to the increased dilution of their industries as more competing vendors emerge. Additionally, in a rapidly moving commercial environment, their products’ shelf lives will shorten. However, their challenges are another story.

Scarcity tells us that if everyone has access to a particular insight, then that insight doesn’t differentiate its owners anymore. Indeed, that insight has become commoditized. In other words, if the whole market can use the same data products, then no discernible signal will be created. Whatever value that insight had has taken a hit with the increase in the addressable market size.

Business value comes from having an edge — knowing the things that others do not. But for the insight provider, by selling the same product to more customers, the value of the product itself decreases. With the achievement of “Silicon Valley” scale, would come the virtual nullification of any business benefit.

For geospatial companies who have expertise in analytic creation, this becomes an exciting challenge. It might mean the production of customized analytics combining commercially available sources as well as proprietary sources. It might mean the creation of customizable analytics, selling geographic exclusivity or even identifying novel data acquisition methods. For those suitably equipped, engaging a commoditized-analytic streetfight might be the attractive path!

In the broader business community, it might involve building geospatial research and development teams with the capabilities to craft bespoke business intelligence solutions from geospatial sources with direct access to vertical domain expertise.

Counter to the recent internet software vendors (ISV) and software-as-a-service (SAAS) cultures, after the creation of enabling geospatial software, the central geospatial analytics play might be the production of highly customized analytics providing the business edge.

The commoditization risk of “insight as a service” may be too high.

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The law of scarcity is a building block of commerce. It suggests that a desirable item in short supply is more valuable than one that is more readily available.

How does this apply to our digital economy? Indeed, how should we think about scarcity when software-as-a-service companies are obsessing over scale and growth?

The dichotomy I want to illustrate here is that of scale vs value. There is an idea with technology companies that we should create something once and then sell it many times. That is true with platform software (e.g., iTunes), but it is not valid with providers of data. With data or the insight derived from it, the value of that insight diminishes with the number of people who have access to it. This is the law of scarcity as it pertains to information.

The maturity of cloud technology has boosted the opportunity that geospatial technology affords the business community. We can build pipelines of data in the sky. We can control the flows of that data with manipulation engines, transformations and fusion techniques. We can identify features from imagery, then use those features to feed machine learning algorithms. We can use those algorithms to filter and interpret torrents of data flowing at a rate orders-of-magnitude higher than a team of humans could consume.

Geospatial can now deliver what we have always promised: the near real-time, remote monitoring and analysis of assets. But how should we be providing this capability? An obvious place for us to look has been the financial markets. Providing traders and analysts with new insight with which to develop “signal.” The commercial geospatial and remote sensing industry, which has been chasing access to the elusive “Bloomberg Terminal,” should, however, consider the pricing effect of abundance.

Of course, there are base data acquisition companies which must exist to support the creation of geospatial analytics. Those are the companies that are creating streams of base data from which the analytics companies are drawing their insight. Products of this nature will always be necessary, indeed foundational for discerning any geospatial insight. Including the satellite, LiDAR, RADAR, metrological and mapping companies, these are the companies that know how to create building blocks from which to derive vertically-focused insight. These companies will remain mostly unchallenged by scarcity, but they will be subject to the increased dilution of their industries as more competing vendors emerge. Additionally, in a rapidly moving commercial environment, their products’ shelf lives will shorten. However, their challenges are another story.

Scarcity tells us that if everyone has access to a particular insight, then that insight doesn’t differentiate its owners anymore. Indeed, that insight has become commoditized. In other words, if the whole market can use the same data products, then no discernible signal will be created. Whatever value that insight had has taken a hit with the increase in the addressable market size.

Business value comes from having an edge — knowing the things that others do not. But for the insight provider, by selling the same product to more customers, the value of the product itself decreases. With the achievement of “Silicon Valley” scale, would come the virtual nullification of any business benefit.

For geospatial companies who have expertise in analytic creation, this becomes an exciting challenge. It might mean the production of customized analytics combining commercially available sources as well as proprietary sources. It might mean the creation of customizable analytics, selling geographic exclusivity or even identifying novel data acquisition methods. For those suitably equipped, engaging a commoditized-analytic streetfight might be the attractive path!

In the broader business community, it might involve building geospatial research and development teams with the capabilities to craft bespoke business intelligence solutions from geospatial sources with direct access to vertical domain expertise.

Counter to the recent internet software vendors (ISV) and software-as-a-service (SAAS) cultures, after the creation of enabling geospatial software, the central geospatial analytics play might be the production of highly customized analytics providing the business edge.

The commoditization risk of “insight as a service” may be too high.

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